Application of grey correlation-based EDAS method for parametric optimization of non-traditional machining processes

Abstract

Higher dimensional accuracy along with better surface finish of various advanced engineering materials has turned out to be the prime desideratum for the present day manufacturing industries. To achieve this, non-traditional machining (NTM) processes have become quite popular because of their ability to produce intricate shape geometries on diverse difficult-to-machine materials. To allow these processes to operate at their fullest capability, it is often recommended to set their different input parameters at the optimal levels. Thus, in this paper, a new technique combining grey correlation method and evaluation based on distance from average solution is applied for simultaneous optimization of three NTM processes, i.e. photochemical machining process, laserassisted jet electrochemical machining process and abrasive water jet drilling process. The derived optimal parametric combinations outperform those as identified by the other popular multi-objective optimization techniques with respect to the considered response values. The results of analysis of variance also identify the most influencing parameters for the said NTM processes. Finally, the developed surface plots would help the process engineers in investigating the effects of different NTM process parameters on the corresponding grey appraisal scores.

A modified EDAS model for comparison of mobile wallet service providers in India

“…Here, ξ is the differentiating or distinguishing or identification coefficient whose value is usually taken as 0.5 as suggested in Das and Chakraborty (2022) which is a neutral position.…”

“…However, EDAS method suffers from a limitation that it uses distance as a measurement scale. The average point may not be decided precisely in a typical scenario which involves substantial amount of subjectivity (Das and Chakraborty 2022). Further, classical EDAS method many a times does not reveal true ranking when comparing the alternatives that are too similar in magnitude or differing from each other largely (Ilieva et al 2018).…”

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

“…Multi-Criteria Decision Making (MCDM) techniques are widely used in various fields of study for process parameter optimization. Das and Chakraborty (Das and Chakraborty, 2022) applied a Grey Correlation-based EDAS technique for PCM, Laser-Assisted Jet Electro-Chemical Machining and Abrasive Water Jet Drilling process optimization. They accurately predicted optimum process parameters and confirmed these using regression equations.…”

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

“…Because of the complexity of the physical phenomena occurring during electrical discharge machining, a significant part of the research has focused on the development of predictive models for the process. One of the most frequently used methodologies that allow us to determine the relationship between the input factors and the results of process optimization are the response surface methodology [ 17 , 18 , 19 , 20 , 21 ], artificial neural networks [ 22 , 23 , 24 ], desirability functions [ 25 , 26 , 27 , 28 ], the fuzzy possibility approach [ 29 , 30 ], and gray relational analysis [ 31 , 32 , 33 ]. The study provided by Jatakar et al [ 34 ] shows that using the ANN algorithm can effectively diagnose and self-monitor complex manufacturing processes without human intervention.…”